Overview

Dataset statistics

Number of variables33
Number of observations200759
Missing cells350667
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory52.1 MiB
Average record size in memory272.0 B

Variable types

Categorical18
DateTime5
Numeric10

Alerts

Valor del Contrato is highly overall correlated with Saldo CDPHigh correlation
Valor Facturado is highly overall correlated with Valor Pendiente de Pago and 2 other fieldsHigh correlation
Valor Pendiente de Pago is highly overall correlated with Valor Facturado and 2 other fieldsHigh correlation
Valor Pagado is highly overall correlated with Valor Facturado and 2 other fieldsHigh correlation
Valor Pendiente de Ejecucion is highly overall correlated with Valor Facturado and 2 other fieldsHigh correlation
Saldo CDP is highly overall correlated with Valor del ContratoHigh correlation
Orden is highly overall correlated with SectorHigh correlation
Sector is highly overall correlated with OrdenHigh correlation
Tipo de Contrato is highly overall correlated with Modalidad de ContratacionHigh correlation
Modalidad de Contratacion is highly overall correlated with Tipo de Contrato and 1 other fieldsHigh correlation
Es Pyme is highly overall correlated with Modalidad de ContratacionHigh correlation
Rama is highly imbalanced (64.6%)Imbalance
Tipo de Contrato is highly imbalanced (76.8%)Imbalance
Modalidad de Contratacion is highly imbalanced (67.8%)Imbalance
Condiciones de Entrega is highly imbalanced (52.3%)Imbalance
Es Grupo is highly imbalanced (94.3%)Imbalance
Habilita Pago Adelantado is highly imbalanced (98.3%)Imbalance
Obligación Ambiental is highly imbalanced (76.4%)Imbalance
EsPostConflicto is highly imbalanced (96.8%)Imbalance
Fecha de Inicio de Ejecucion has 175260 (87.3%) missing valuesMissing
Fecha de Fin de Ejecucion has 175043 (87.2%) missing valuesMissing
Valor del Contrato is highly skewed (γ1 = 396.339426)Skewed
Valor Facturado is highly skewed (γ1 = 440.8915116)Skewed
Valor Pendiente de Pago is highly skewed (γ1 = 206.5018514)Skewed
Valor Pagado is highly skewed (γ1 = 440.6712478)Skewed
Valor Pendiente de Ejecucion is highly skewed (γ1 = 98.58376524)Skewed
Saldo CDP is highly skewed (γ1 = 170.4036904)Skewed
Dias Adicionados is highly skewed (γ1 = 34.72227299)Skewed
Presupuesto General de la Nacion – PGN is highly skewed (γ1 = 432.1474784)Skewed
Recursos Propios (Alcaldías, Gobernaciones y Resguardos Indígenas) is highly skewed (γ1 = 212.2501029)Skewed
Recursos Propios is highly skewed (γ1 = 156.5093127)Skewed
Valor Facturado has 83466 (41.6%) zerosZeros
Valor Pendiente de Pago has 73931 (36.8%) zerosZeros
Valor Pagado has 94006 (46.8%) zerosZeros
Valor Pendiente de Ejecucion has 73496 (36.6%) zerosZeros
Saldo CDP has 10804 (5.4%) zerosZeros
Dias Adicionados has 199682 (99.5%) zerosZeros
Presupuesto General de la Nacion – PGN has 162435 (80.9%) zerosZeros
Recursos Propios (Alcaldías, Gobernaciones y Resguardos Indígenas) has 110781 (55.2%) zerosZeros
Recursos Propios has 166430 (82.9%) zerosZeros

Reproduction

Analysis started2023-07-19 00:13:01.611328
Analysis finished2023-07-19 00:14:05.183479
Duration1 minute and 3.57 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

Departamento
Categorical

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Distrito Capital de Bogotá
58831 
Valle del Cauca
23601 
Antioquia
20089 
Santander
14911 
Meta
9354 
Other values (29)
73973 

Length

Max length40
Median length26
Mean length14.416724
Min length4

Characters and Unicode

Total characters2894287
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDistrito Capital de Bogotá
2nd rowValle del Cauca
3rd rowDistrito Capital de Bogotá
4th rowBoyacá
5th rowRisaralda

Common Values

ValueCountFrequency (%)
Distrito Capital de Bogotá 58831
29.3%
Valle del Cauca 23601
11.8%
Antioquia 20089
 
10.0%
Santander 14911
 
7.4%
Meta 9354
 
4.7%
Boyacá 8774
 
4.4%
Risaralda 6816
 
3.4%
Cundinamarca 6585
 
3.3%
Huila 5857
 
2.9%
Casanare 5572
 
2.8%
Other values (24) 40369
20.1%

Length

2023-07-18T19:14:05.399551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 63784
14.3%
distrito 58831
13.2%
capital 58831
13.2%
bogotá 58831
13.2%
cauca 26058
 
5.9%
valle 23601
 
5.3%
del 23601
 
5.3%
antioquia 20089
 
4.5%
santander 19864
 
4.5%
meta 9354
 
2.1%
Other values (35) 81906
18.4%

Most occurring characters

ValueCountFrequency (%)
a 379676
13.1%
t 301588
 
10.4%
i 256392
 
8.9%
243991
 
8.4%
o 232236
 
8.0%
l 160618
 
5.5%
e 159463
 
5.5%
d 134681
 
4.7%
r 118137
 
4.1%
C 106987
 
3.7%
Other values (35) 800518
27.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2292931
79.2%
Uppercase Letter 355523
 
12.3%
Space Separator 243991
 
8.4%
Other Punctuation 1842
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 379676
16.6%
t 301588
13.2%
i 256392
11.2%
o 232236
10.1%
l 160618
7.0%
e 159463
7.0%
d 134681
 
5.9%
r 118137
 
5.2%
n 97475
 
4.3%
s 79133
 
3.5%
Other values (18) 373532
16.3%
Uppercase Letter
ValueCountFrequency (%)
C 106987
30.1%
B 71681
20.2%
D 59614
16.8%
A 27167
 
7.6%
S 24115
 
6.8%
V 23880
 
6.7%
M 11149
 
3.1%
N 7545
 
2.1%
R 6816
 
1.9%
H 5857
 
1.6%
Other values (5) 10712
 
3.0%
Space Separator
ValueCountFrequency (%)
243991
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1842
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2648454
91.5%
Common 245833
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 379676
14.3%
t 301588
11.4%
i 256392
 
9.7%
o 232236
 
8.8%
l 160618
 
6.1%
e 159463
 
6.0%
d 134681
 
5.1%
r 118137
 
4.5%
C 106987
 
4.0%
n 97475
 
3.7%
Other values (33) 701201
26.5%
Common
ValueCountFrequency (%)
243991
99.3%
, 1842
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2809502
97.1%
None 84785
 
2.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 379676
13.5%
t 301588
10.7%
i 256392
 
9.1%
243991
 
8.7%
o 232236
 
8.3%
l 160618
 
5.7%
e 159463
 
5.7%
d 134681
 
4.8%
r 118137
 
4.2%
C 106987
 
3.8%
Other values (30) 715733
25.5%
None
ValueCountFrequency (%)
á 72120
85.1%
í 7532
 
8.9%
é 1991
 
2.3%
ñ 1809
 
2.1%
ó 1333
 
1.6%

Orden
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Territorial
127222 
Nacional
70325 
Corporación Autónoma
 
3212

Length

Max length20
Median length11
Mean length10.093107
Min length8

Characters and Unicode

Total characters2026282
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTerritorial
2nd rowTerritorial
3rd rowTerritorial
4th rowTerritorial
5th rowTerritorial

Common Values

ValueCountFrequency (%)
Territorial 127222
63.4%
Nacional 70325
35.0%
Corporación Autónoma 3212
 
1.6%

Length

2023-07-18T19:14:05.617553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-18T19:14:05.900584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
territorial 127222
62.4%
nacional 70325
34.5%
corporación 3212
 
1.6%
autónoma 3212
 
1.6%

Most occurring characters

ValueCountFrequency (%)
r 388090
19.2%
i 327981
16.2%
a 274296
13.5%
o 207183
10.2%
l 197547
9.7%
t 130434
 
6.4%
T 127222
 
6.3%
e 127222
 
6.3%
n 76749
 
3.8%
c 73537
 
3.6%
Other values (8) 96021
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1819099
89.8%
Uppercase Letter 203971
 
10.1%
Space Separator 3212
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 388090
21.3%
i 327981
18.0%
a 274296
15.1%
o 207183
11.4%
l 197547
10.9%
t 130434
 
7.2%
e 127222
 
7.0%
n 76749
 
4.2%
c 73537
 
4.0%
ó 6424
 
0.4%
Other values (3) 9636
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
T 127222
62.4%
N 70325
34.5%
C 3212
 
1.6%
A 3212
 
1.6%
Space Separator
ValueCountFrequency (%)
3212
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2023070
99.8%
Common 3212
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 388090
19.2%
i 327981
16.2%
a 274296
13.6%
o 207183
10.2%
l 197547
9.8%
t 130434
 
6.4%
T 127222
 
6.3%
e 127222
 
6.3%
n 76749
 
3.8%
c 73537
 
3.6%
Other values (7) 92809
 
4.6%
Common
ValueCountFrequency (%)
3212
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2019858
99.7%
None 6424
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 388090
19.2%
i 327981
16.2%
a 274296
13.6%
o 207183
10.3%
l 197547
9.8%
t 130434
 
6.5%
T 127222
 
6.3%
e 127222
 
6.3%
n 76749
 
3.8%
c 73537
 
3.6%
Other values (7) 89597
 
4.4%
None
ValueCountFrequency (%)
ó 6424
100.0%

Sector
Categorical

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Servicio Público
53134 
No aplica/No pertenece
31793 
Salud y Protección Social
20832 
Trabajo
16373 
defensa
12535 
Other values (20)
66092 

Length

Max length50
Median length33
Mean length17.449723
Min length7

Characters and Unicode

Total characters3503189
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPlaneación
2nd rowdeportes
3rd rowSalud y Protección Social
4th rowServicio Público
5th rowServicio Público

Common Values

ValueCountFrequency (%)
Servicio Público 53134
26.5%
No aplica/No pertenece 31793
15.8%
Salud y Protección Social 20832
 
10.4%
Trabajo 16373
 
8.2%
defensa 12535
 
6.2%
Ambiente y Desarrollo Sostenible 10499
 
5.2%
Educación Nacional 9876
 
4.9%
deportes 6439
 
3.2%
Cultura 6415
 
3.2%
Transporte 5542
 
2.8%
Other values (15) 27321
13.6%

Length

2023-07-18T19:14:06.131626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
público 56300
12.0%
servicio 53134
 
11.3%
y 43216
 
9.2%
no 31793
 
6.8%
aplica/no 31793
 
6.8%
pertenece 31793
 
6.8%
social 23674
 
5.0%
salud 20832
 
4.4%
protección 20832
 
4.4%
trabajo 16373
 
3.5%
Other values (40) 140673
29.9%

Most occurring characters

ValueCountFrequency (%)
i 344161
 
9.8%
e 330848
 
9.4%
o 310064
 
8.9%
c 302221
 
8.6%
269654
 
7.7%
a 255513
 
7.3%
l 197048
 
5.6%
r 194550
 
5.6%
n 152337
 
4.3%
t 111396
 
3.2%
Other values (33) 1035397
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2831826
80.8%
Uppercase Letter 365989
 
10.4%
Space Separator 269654
 
7.7%
Other Punctuation 35720
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 344161
12.2%
e 330848
11.7%
o 310064
10.9%
c 302221
10.7%
a 255513
9.0%
l 197048
 
7.0%
r 194550
 
6.9%
n 152337
 
5.4%
t 111396
 
3.9%
b 94211
 
3.3%
Other values (15) 539477
19.1%
Uppercase Letter
ValueCountFrequency (%)
S 108139
29.5%
P 81385
22.2%
N 73462
20.1%
T 27475
 
7.5%
C 15146
 
4.1%
E 12864
 
3.5%
A 10499
 
2.9%
D 10499
 
2.9%
I 7605
 
2.1%
V 3927
 
1.1%
Other values (5) 14988
 
4.1%
Other Punctuation
ValueCountFrequency (%)
/ 31793
89.0%
, 3927
 
11.0%
Space Separator
ValueCountFrequency (%)
269654
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3197815
91.3%
Common 305374
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 344161
 
10.8%
e 330848
 
10.3%
o 310064
 
9.7%
c 302221
 
9.5%
a 255513
 
8.0%
l 197048
 
6.2%
r 194550
 
6.1%
n 152337
 
4.8%
t 111396
 
3.5%
S 108139
 
3.4%
Other values (30) 891538
27.9%
Common
ValueCountFrequency (%)
269654
88.3%
/ 31793
 
10.4%
, 3927
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3396009
96.9%
None 107180
 
3.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 344161
 
10.1%
e 330848
 
9.7%
o 310064
 
9.1%
c 302221
 
8.9%
269654
 
7.9%
a 255513
 
7.5%
l 197048
 
5.8%
r 194550
 
5.7%
n 152337
 
4.5%
t 111396
 
3.3%
Other values (29) 928217
27.3%
None
ValueCountFrequency (%)
ú 56840
53.0%
ó 42814
39.9%
í 4355
 
4.1%
é 3171
 
3.0%

Rama
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Ejecutivo
170273 
Corporación Autónoma
27130 
Judicial
 
1752
Legislativo
 
1604

Length

Max length20
Median length9
Mean length10.493761
Min length8

Characters and Unicode

Total characters2106717
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEjecutivo
2nd rowEjecutivo
3rd rowEjecutivo
4th rowEjecutivo
5th rowEjecutivo

Common Values

ValueCountFrequency (%)
Ejecutivo 170273
84.8%
Corporación Autónoma 27130
 
13.5%
Judicial 1752
 
0.9%
Legislativo 1604
 
0.8%

Length

2023-07-18T19:14:06.337417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-18T19:14:06.589411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ejecutivo 170273
74.7%
corporación 27130
 
11.9%
autónoma 27130
 
11.9%
judicial 1752
 
0.8%
legislativo 1604
 
0.7%

Most occurring characters

ValueCountFrequency (%)
o 253267
12.0%
i 204115
9.7%
c 199155
9.5%
u 199155
9.5%
t 199007
9.4%
e 171877
8.2%
v 171877
8.2%
E 170273
8.1%
j 170273
8.1%
a 57616
 
2.7%
Other values (14) 310102
14.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1851698
87.9%
Uppercase Letter 227889
 
10.8%
Space Separator 27130
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 253267
13.7%
i 204115
11.0%
c 199155
10.8%
u 199155
10.8%
t 199007
10.7%
e 171877
9.3%
v 171877
9.3%
j 170273
9.2%
a 57616
 
3.1%
r 54260
 
2.9%
Other values (8) 171096
9.2%
Uppercase Letter
ValueCountFrequency (%)
E 170273
74.7%
C 27130
 
11.9%
A 27130
 
11.9%
J 1752
 
0.8%
L 1604
 
0.7%
Space Separator
ValueCountFrequency (%)
27130
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2079587
98.7%
Common 27130
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 253267
12.2%
i 204115
9.8%
c 199155
9.6%
u 199155
9.6%
t 199007
9.6%
e 171877
8.3%
v 171877
8.3%
E 170273
8.2%
j 170273
8.2%
a 57616
 
2.8%
Other values (13) 282972
13.6%
Common
ValueCountFrequency (%)
27130
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2052457
97.4%
None 54260
 
2.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 253267
12.3%
i 204115
9.9%
c 199155
9.7%
u 199155
9.7%
t 199007
9.7%
e 171877
8.4%
v 171877
8.4%
E 170273
8.3%
j 170273
8.3%
a 57616
 
2.8%
Other values (13) 255842
12.5%
None
ValueCountFrequency (%)
ó 54260
100.0%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Descentralizada
105639 
Centralizada
86861 
No Definido
 
8259

Length

Max length15
Median length15
Mean length13.537455
Min length11

Characters and Unicode

Total characters2717766
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentralizada
2nd rowDescentralizada
3rd rowDescentralizada
4th rowDescentralizada
5th rowDescentralizada

Common Values

ValueCountFrequency (%)
Descentralizada 105639
52.6%
Centralizada 86861
43.3%
No Definido 8259
 
4.1%

Length

2023-07-18T19:14:06.808412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-18T19:14:07.064293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
descentralizada 105639
50.5%
centralizada 86861
41.6%
no 8259
 
4.0%
definido 8259
 
4.0%

Most occurring characters

ValueCountFrequency (%)
a 577500
21.2%
e 306398
11.3%
i 209018
 
7.7%
d 200759
 
7.4%
n 200759
 
7.4%
l 192500
 
7.1%
z 192500
 
7.1%
t 192500
 
7.1%
r 192500
 
7.1%
D 113898
 
4.2%
Other values (7) 339434
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2500489
92.0%
Uppercase Letter 209018
 
7.7%
Space Separator 8259
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 577500
23.1%
e 306398
12.3%
i 209018
 
8.4%
d 200759
 
8.0%
n 200759
 
8.0%
l 192500
 
7.7%
z 192500
 
7.7%
t 192500
 
7.7%
r 192500
 
7.7%
c 105639
 
4.2%
Other values (3) 130416
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
D 113898
54.5%
C 86861
41.6%
N 8259
 
4.0%
Space Separator
ValueCountFrequency (%)
8259
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2709507
99.7%
Common 8259
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 577500
21.3%
e 306398
11.3%
i 209018
 
7.7%
d 200759
 
7.4%
n 200759
 
7.4%
l 192500
 
7.1%
z 192500
 
7.1%
t 192500
 
7.1%
r 192500
 
7.1%
D 113898
 
4.2%
Other values (6) 331175
12.2%
Common
ValueCountFrequency (%)
8259
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2717766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 577500
21.2%
e 306398
11.3%
i 209018
 
7.7%
d 200759
 
7.4%
n 200759
 
7.4%
l 192500
 
7.1%
z 192500
 
7.1%
t 192500
 
7.1%
r 192500
 
7.1%
D 113898
 
4.2%
Other values (7) 339434
12.5%

Estado Contrato
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
terminado
125519 
Cerrado
66538 
cedido
 
7398
Suspendido
 
1304

Length

Max length10
Median length9
Mean length8.2330805
Min length6

Characters and Unicode

Total characters1652865
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowterminado
2nd rowCerrado
3rd rowterminado
4th rowterminado
5th rowCerrado

Common Values

ValueCountFrequency (%)
terminado 125519
62.5%
Cerrado 66538
33.1%
cedido 7398
 
3.7%
Suspendido 1304
 
0.6%

Length

2023-07-18T19:14:07.309807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-18T19:14:07.632807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
terminado 125519
62.5%
cerrado 66538
33.1%
cedido 7398
 
3.7%
suspendido 1304
 
0.6%

Most occurring characters

ValueCountFrequency (%)
r 258595
15.6%
d 209461
12.7%
e 200759
12.1%
o 200759
12.1%
a 192057
11.6%
i 134221
8.1%
n 126823
7.7%
t 125519
7.6%
m 125519
7.6%
C 66538
 
4.0%
Other values (5) 12614
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1585023
95.9%
Uppercase Letter 67842
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 258595
16.3%
d 209461
13.2%
e 200759
12.7%
o 200759
12.7%
a 192057
12.1%
i 134221
8.5%
n 126823
8.0%
t 125519
7.9%
m 125519
7.9%
c 7398
 
0.5%
Other values (3) 3912
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
C 66538
98.1%
S 1304
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 1652865
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 258595
15.6%
d 209461
12.7%
e 200759
12.1%
o 200759
12.1%
a 192057
11.6%
i 134221
8.1%
n 126823
7.7%
t 125519
7.6%
m 125519
7.6%
C 66538
 
4.0%
Other values (5) 12614
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1652865
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 258595
15.6%
d 209461
12.7%
e 200759
12.1%
o 200759
12.1%
a 192057
11.6%
i 134221
8.1%
n 126823
7.7%
t 125519
7.6%
m 125519
7.6%
C 66538
 
4.0%
Other values (5) 12614
 
0.8%

Tipo de Contrato
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Prestación de servicios
170592 
Otro
 
8209
Suministros
 
6364
Compraventa
 
5776
DecreeLaw092/2017
 
4752
Other values (16)
 
5066

Length

Max length26
Median length23
Mean length21.126674
Min length4

Characters and Unicode

Total characters4241370
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrestación de servicios
2nd rowPrestación de servicios
3rd rowPrestación de servicios
4th rowPrestación de servicios
5th rowPrestación de servicios

Common Values

ValueCountFrequency (%)
Prestación de servicios 170592
85.0%
Otro 8209
 
4.1%
Suministros 6364
 
3.2%
Compraventa 5776
 
2.9%
DecreeLaw092/2017 4752
 
2.4%
Arrendamiento de inmuebles 1612
 
0.8%
Obra 1604
 
0.8%
Consultoría 418
 
0.2%
No Especificado 416
 
0.2%
Interventoría 376
 
0.2%
Other values (11) 640
 
0.3%

Length

2023-07-18T19:14:07.896008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 172269
31.6%
servicios 170618
31.3%
prestación 170592
31.3%
otro 8209
 
1.5%
suministros 6364
 
1.2%
compraventa 5776
 
1.1%
decreelaw092/2017 4752
 
0.9%
arrendamiento 1677
 
0.3%
inmuebles 1614
 
0.3%
obra 1604
 
0.3%
Other values (17) 2296
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 541679
12.8%
i 528799
12.5%
s 527316
12.4%
r 372694
8.8%
c 346851
8.2%
345012
8.1%
o 195856
 
4.6%
t 194110
 
4.6%
a 191748
 
4.5%
n 188955
 
4.5%
Other values (30) 808350
19.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3652401
86.1%
Space Separator 345012
 
8.1%
Uppercase Letter 205941
 
4.9%
Decimal Number 33264
 
0.8%
Other Punctuation 4752
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 541679
14.8%
i 528799
14.5%
s 527316
14.4%
r 372694
10.2%
c 346851
9.5%
o 195856
 
5.4%
t 194110
 
5.3%
a 191748
 
5.2%
n 188955
 
5.2%
v 176777
 
4.8%
Other values (12) 387616
10.6%
Uppercase Letter
ValueCountFrequency (%)
P 170606
82.8%
O 9813
 
4.8%
S 6602
 
3.2%
C 6500
 
3.2%
D 4752
 
2.3%
L 4752
 
2.3%
A 1684
 
0.8%
E 422
 
0.2%
N 420
 
0.2%
I 376
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 9504
28.6%
0 9504
28.6%
7 4752
14.3%
1 4752
14.3%
9 4752
14.3%
Space Separator
ValueCountFrequency (%)
345012
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 4752
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3858342
91.0%
Common 383028
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 541679
14.0%
i 528799
13.7%
s 527316
13.7%
r 372694
9.7%
c 346851
9.0%
o 195856
 
5.1%
t 194110
 
5.0%
a 191748
 
5.0%
n 188955
 
4.9%
v 176777
 
4.6%
Other values (23) 593557
15.4%
Common
ValueCountFrequency (%)
345012
90.1%
2 9504
 
2.5%
0 9504
 
2.5%
7 4752
 
1.2%
1 4752
 
1.2%
/ 4752
 
1.2%
9 4752
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4069960
96.0%
None 171410
 
4.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 541679
13.3%
i 528799
13.0%
s 527316
13.0%
r 372694
9.2%
c 346851
8.5%
345012
8.5%
o 195856
 
4.8%
t 194110
 
4.8%
a 191748
 
4.7%
n 188955
 
4.6%
Other values (27) 636940
15.6%
None
ValueCountFrequency (%)
ó 170609
99.5%
í 794
 
0.5%
ú 7
 
< 0.1%

Modalidad de Contratacion
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Contratación directa
155358 
Contratación régimen especial
19412 
Mínima cuantía
15734 
Selección Abreviada de Menor Cuantía
 
2588
Selección abreviada subasta inversa
 
2513
Other values (10)
 
5154

Length

Max length59
Median length20
Mean length21.135745
Min length11

Characters and Unicode

Total characters4243191
Distinct characters42
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowContratación directa
2nd rowContratación directa
3rd rowContratación régimen especial
4th rowContratación directa
5th rowContratación directa

Common Values

ValueCountFrequency (%)
Contratación directa 155358
77.4%
Contratación régimen especial 19412
 
9.7%
Mínima cuantía 15734
 
7.8%
Selección Abreviada de Menor Cuantía 2588
 
1.3%
Selección abreviada subasta inversa 2513
 
1.3%
Contratación Directa (con ofertas) 2165
 
1.1%
Contratación régimen especial (con ofertas) 1264
 
0.6%
Licitación pública 515
 
0.3%
No Definido 320
 
0.2%
Licitación pública Obra Publica 318
 
0.2%
Other values (5) 572
 
0.3%

Length

2023-07-18T19:14:08.169178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
contratación 178199
40.2%
directa 157523
35.5%
régimen 20676
 
4.7%
especial 20676
 
4.7%
cuantía 18322
 
4.1%
mínima 15734
 
3.5%
abreviada 5161
 
1.2%
selección 5101
 
1.2%
con 3443
 
0.8%
ofertas 3429
 
0.8%
Other values (23) 14997
 
3.4%

Most occurring characters

ValueCountFrequency (%)
a 614053
14.5%
t 540387
12.7%
n 427090
10.1%
i 411385
9.7%
c 389295
9.2%
r 372236
8.8%
e 247782
 
5.8%
242502
 
5.7%
o 190677
 
4.5%
ó 184147
 
4.3%
Other values (32) 623637
14.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3778443
89.0%
Space Separator 242502
 
5.7%
Uppercase Letter 213508
 
5.0%
Open Punctuation 3429
 
0.1%
Close Punctuation 3429
 
0.1%
Connector Punctuation 940
 
< 0.1%
Decimal Number 564
 
< 0.1%
Dash Punctuation 376
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 614053
16.3%
t 540387
14.3%
n 427090
11.3%
i 411385
10.9%
c 389295
10.3%
r 372236
9.9%
e 247782
6.6%
o 190677
 
5.0%
ó 184147
 
4.9%
d 163760
 
4.3%
Other values (13) 237631
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
C 181909
85.2%
M 18630
 
8.7%
S 5597
 
2.6%
A 2648
 
1.2%
D 2485
 
1.2%
L 1021
 
0.5%
N 320
 
0.1%
P 318
 
0.1%
O 318
 
0.1%
E 202
 
0.1%
Decimal Number
ValueCountFrequency (%)
2 188
33.3%
0 188
33.3%
1 188
33.3%
Space Separator
ValueCountFrequency (%)
242502
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3429
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3429
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 940
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 376
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3991951
94.1%
Common 251240
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 614053
15.4%
t 540387
13.5%
n 427090
10.7%
i 411385
10.3%
c 389295
9.8%
r 372236
9.3%
e 247782
6.2%
o 190677
 
4.8%
ó 184147
 
4.6%
C 181909
 
4.6%
Other values (24) 432990
10.8%
Common
ValueCountFrequency (%)
242502
96.5%
( 3429
 
1.4%
) 3429
 
1.4%
_ 940
 
0.4%
- 376
 
0.1%
2 188
 
0.1%
0 188
 
0.1%
1 188
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4003169
94.3%
None 240022
 
5.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 614053
15.3%
t 540387
13.5%
n 427090
10.7%
i 411385
10.3%
c 389295
9.7%
r 372236
9.3%
e 247782
6.2%
242502
 
6.1%
o 190677
 
4.8%
C 181909
 
4.5%
Other values (28) 385853
9.6%
None
ValueCountFrequency (%)
ó 184147
76.7%
í 34056
 
14.2%
é 20986
 
8.7%
ú 833
 
0.3%
Distinct1551
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Minimum2018-01-25 00:00:00
Maximum2023-07-08 00:00:00
2023-07-18T19:14:08.771452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:14:09.025361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1552
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Minimum2019-01-01 00:00:00
Maximum2023-10-03 00:00:00
2023-07-18T19:14:09.309995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:14:09.574584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1957
Distinct (%)1.0%
Missing2
Missing (%)< 0.1%
Memory size3.1 MiB
Minimum2019-01-02 00:00:00
Maximum2042-02-23 00:00:00
2023-07-18T19:14:09.851405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:14:10.101583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct552
Distinct (%)2.2%
Missing175260
Missing (%)87.3%
Memory size3.1 MiB
Minimum2009-01-18 00:00:00
Maximum2021-10-25 00:00:00
2023-07-18T19:14:10.351391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:14:10.600131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct934
Distinct (%)3.6%
Missing175043
Missing (%)87.2%
Memory size3.1 MiB
Minimum2018-12-07 00:00:00
Maximum2034-11-21 00:00:00
2023-07-18T19:14:10.884499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:14:11.158505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
A convenir
80963 
Como acordado previamente
65375 
No Definido
42134 
Transporte incluido
10892 
Transporte no incluído
 
647
Other values (9)
 
748

Length

Max length58
Median length56
Mean length15.774426
Min length10

Characters and Unicode

Total characters3166858
Distinct characters38
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowNo Definido
2nd rowComo acordado previamente
3rd rowA convenir
4th rowA convenir
5th rowA convenir

Common Values

ValueCountFrequency (%)
A convenir 80963
40.3%
Como acordado previamente 65375
32.6%
No Definido 42134
21.0%
Transporte incluido 10892
 
5.4%
Transporte no incluído 647
 
0.3%
DAP - Entregado en un punto (lugar de destino convenido) 588
 
0.3%
Transporte a cargo del comprador 115
 
0.1%
DAT - Delivery at terminal 13
 
< 0.1%
CPT - Transporte pagado hasta (lugar de destino convenido) 11
 
< 0.1%
CIP - Carriage and insurance paid to 11
 
< 0.1%
Other values (4) 10
 
< 0.1%

Length

2023-07-18T19:14:11.361500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 81078
17.1%
convenir 80963
17.1%
como 65375
13.8%
acordado 65375
13.8%
previamente 65375
13.8%
no 42781
9.0%
definido 42134
8.9%
transporte 11665
 
2.5%
incluido 10892
 
2.3%
incluído 647
 
0.1%
Other values (37) 6502
 
1.4%

Most occurring characters

ValueCountFrequency (%)
o 453928
14.3%
e 334053
10.5%
n 297486
9.4%
272028
8.6%
i 254303
8.0%
r 236647
 
7.5%
a 209456
 
6.6%
d 187079
 
5.9%
c 158720
 
5.0%
v 146953
 
4.6%
Other values (28) 616205
19.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2648204
83.6%
Space Separator 272028
 
8.6%
Uppercase Letter 244794
 
7.7%
Dash Punctuation 634
 
< 0.1%
Open Punctuation 599
 
< 0.1%
Close Punctuation 599
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 453928
17.1%
e 334053
12.6%
n 297486
11.2%
i 254303
9.6%
r 236647
8.9%
a 209456
7.9%
d 187079
7.1%
c 158720
 
6.0%
v 146953
 
5.5%
m 130878
 
4.9%
Other values (12) 238701
9.0%
Uppercase Letter
ValueCountFrequency (%)
A 81565
33.3%
C 65420
26.7%
D 42757
17.5%
N 42134
17.2%
T 11689
 
4.8%
P 613
 
0.3%
E 590
 
0.2%
I 11
 
< 0.1%
F 7
 
< 0.1%
R 5
 
< 0.1%
Other values (2) 3
 
< 0.1%
Space Separator
ValueCountFrequency (%)
272028
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 634
100.0%
Open Punctuation
ValueCountFrequency (%)
( 599
100.0%
Close Punctuation
ValueCountFrequency (%)
) 599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2892998
91.4%
Common 273860
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 453928
15.7%
e 334053
11.5%
n 297486
10.3%
i 254303
8.8%
r 236647
8.2%
a 209456
7.2%
d 187079
 
6.5%
c 158720
 
5.5%
v 146953
 
5.1%
m 130878
 
4.5%
Other values (24) 483495
16.7%
Common
ValueCountFrequency (%)
272028
99.3%
- 634
 
0.2%
( 599
 
0.2%
) 599
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3166211
> 99.9%
None 647
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 453928
14.3%
e 334053
10.6%
n 297486
9.4%
272028
8.6%
i 254303
8.0%
r 236647
 
7.5%
a 209456
 
6.6%
d 187079
 
5.9%
c 158720
 
5.0%
v 146953
 
4.6%
Other values (27) 615558
19.4%
None
ValueCountFrequency (%)
í 647
100.0%

Es Grupo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
No
199448 
Si
 
1311

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters401518
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 199448
99.3%
Si 1311
 
0.7%

Length

2023-07-18T19:14:11.559305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-18T19:14:11.744769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 199448
99.3%
si 1311
 
0.7%

Most occurring characters

ValueCountFrequency (%)
N 199448
49.7%
o 199448
49.7%
S 1311
 
0.3%
i 1311
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 200759
50.0%
Lowercase Letter 200759
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 199448
99.3%
S 1311
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
o 199448
99.3%
i 1311
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 401518
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 199448
49.7%
o 199448
49.7%
S 1311
 
0.3%
i 1311
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 401518
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 199448
49.7%
o 199448
49.7%
S 1311
 
0.3%
i 1311
 
0.3%

Es Pyme
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
No
163189 
Si
37570 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters401518
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowSi
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 163189
81.3%
Si 37570
 
18.7%

Length

2023-07-18T19:14:11.916934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-18T19:14:12.149321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 163189
81.3%
si 37570
 
18.7%

Most occurring characters

ValueCountFrequency (%)
N 163189
40.6%
o 163189
40.6%
S 37570
 
9.4%
i 37570
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 200759
50.0%
Lowercase Letter 200759
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 163189
81.3%
S 37570
 
18.7%
Lowercase Letter
ValueCountFrequency (%)
o 163189
81.3%
i 37570
 
18.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 401518
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 163189
40.6%
o 163189
40.6%
S 37570
 
9.4%
i 37570
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 401518
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 163189
40.6%
o 163189
40.6%
S 37570
 
9.4%
i 37570
 
9.4%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
No
200276 
No Definido
 
363
Si
 
120

Length

Max length11
Median length2
Mean length2.0162732
Min length2

Characters and Unicode

Total characters404785
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 200276
99.8%
No Definido 363
 
0.2%
Si 120
 
0.1%

Length

2023-07-18T19:14:12.335485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-18T19:14:12.594688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 200639
99.8%
definido 363
 
0.2%
si 120
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 201002
49.7%
N 200639
49.6%
i 846
 
0.2%
363
 
0.1%
D 363
 
0.1%
e 363
 
0.1%
f 363
 
0.1%
n 363
 
0.1%
d 363
 
0.1%
S 120
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 203300
50.2%
Uppercase Letter 201122
49.7%
Space Separator 363
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 201002
98.9%
i 846
 
0.4%
e 363
 
0.2%
f 363
 
0.2%
n 363
 
0.2%
d 363
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
N 200639
99.8%
D 363
 
0.2%
S 120
 
0.1%
Space Separator
ValueCountFrequency (%)
363
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 404422
99.9%
Common 363
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 201002
49.7%
N 200639
49.6%
i 846
 
0.2%
D 363
 
0.1%
e 363
 
0.1%
f 363
 
0.1%
n 363
 
0.1%
d 363
 
0.1%
S 120
 
< 0.1%
Common
ValueCountFrequency (%)
363
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 404785
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 201002
49.7%
N 200639
49.6%
i 846
 
0.2%
363
 
0.1%
D 363
 
0.1%
e 363
 
0.1%
f 363
 
0.1%
n 363
 
0.1%
d 363
 
0.1%
S 120
 
< 0.1%

Liquidación
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
No
171309 
Si
29450 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters401518
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 171309
85.3%
Si 29450
 
14.7%

Length

2023-07-18T19:14:12.799680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-18T19:14:13.058681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 171309
85.3%
si 29450
 
14.7%

Most occurring characters

ValueCountFrequency (%)
N 171309
42.7%
o 171309
42.7%
S 29450
 
7.3%
i 29450
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 200759
50.0%
Lowercase Letter 200759
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 171309
85.3%
S 29450
 
14.7%
Lowercase Letter
ValueCountFrequency (%)
o 171309
85.3%
i 29450
 
14.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 401518
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 171309
42.7%
o 171309
42.7%
S 29450
 
7.3%
i 29450
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 401518
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 171309
42.7%
o 171309
42.7%
S 29450
 
7.3%
i 29450
 
7.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
No
192994 
Si
 
7765

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters401518
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 192994
96.1%
Si 7765
 
3.9%

Length

2023-07-18T19:14:13.247119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-18T19:14:13.460289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 192994
96.1%
si 7765
 
3.9%

Most occurring characters

ValueCountFrequency (%)
N 192994
48.1%
o 192994
48.1%
S 7765
 
1.9%
i 7765
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 200759
50.0%
Lowercase Letter 200759
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 192994
96.1%
S 7765
 
3.9%
Lowercase Letter
ValueCountFrequency (%)
o 192994
96.1%
i 7765
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 401518
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 192994
48.1%
o 192994
48.1%
S 7765
 
1.9%
i 7765
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 401518
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 192994
48.1%
o 192994
48.1%
S 7765
 
1.9%
i 7765
 
1.9%

Valor del Contrato
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct83739
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1299236 × 108
Minimum0
Maximum1.7997072 × 1012
Zeros867
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-07-18T19:14:13.680297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3200000
Q19462000
median18569870
Q337700000
95-th percentile2.1261877 × 108
Maximum1.7997072 × 1012
Range1.7997072 × 1012
Interquartile range (IQR)28238000

Descriptive statistics

Standard deviation4.1908862 × 109
Coefficient of variation (CV)37.089995
Kurtosis169430.28
Mean1.1299236 × 108
Median Absolute Deviation (MAD)11397116
Skewness396.33943
Sum2.2684234 × 1013
Variance1.7563527 × 1019
MonotonicityNot monotonic
2023-07-18T19:14:13.958662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12000000 1702
 
0.8%
9000000 1476
 
0.7%
6000000 1468
 
0.7%
15000000 1361
 
0.7%
18000000 1259
 
0.6%
10000000 1247
 
0.6%
21000000 1040
 
0.5%
8000000 942
 
0.5%
20000000 886
 
0.4%
0 867
 
0.4%
Other values (83729) 188511
93.9%
ValueCountFrequency (%)
0 867
0.4%
1 5
 
< 0.1%
4 1
 
< 0.1%
34 1
 
< 0.1%
990 1
 
< 0.1%
3670 1
 
< 0.1%
7790 1
 
< 0.1%
9149 1
 
< 0.1%
13685 1
 
< 0.1%
13800 1
 
< 0.1%
ValueCountFrequency (%)
1.79970725 × 10121
< 0.1%
2.133450183 × 10111
< 0.1%
1.865070701 × 10111
< 0.1%
1.432822371 × 10111
< 0.1%
1.179277155 × 10111
< 0.1%
9.699 × 10101
< 0.1%
9.518281839 × 10101
< 0.1%
9.468352547 × 10101
< 0.1%
8.487290598 × 10101
< 0.1%
8.022376125 × 10101
< 0.1%

Valor Facturado
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct56981
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44617793
Minimum0
Maximum1.7906829 × 1012
Zeros83466
Zeros (%)41.6%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-07-18T19:14:14.239423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5724000
Q320429991
95-th percentile66990953
Maximum1.7906829 × 1012
Range1.7906829 × 1012
Interquartile range (IQR)20429991

Descriptive statistics

Standard deviation4.0181841 × 109
Coefficient of variation (CV)90.057887
Kurtosis196447.79
Mean44617793
Median Absolute Deviation (MAD)5724000
Skewness440.89151
Sum8.9574235 × 1012
Variance1.6145804 × 1019
MonotonicityNot monotonic
2023-07-18T19:14:14.496455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 83466
41.6%
12000000 983
 
0.5%
6000000 936
 
0.5%
15000000 824
 
0.4%
9000000 810
 
0.4%
18000000 736
 
0.4%
10000000 735
 
0.4%
8000000 600
 
0.3%
21000000 562
 
0.3%
7500000 540
 
0.3%
Other values (56971) 110567
55.1%
ValueCountFrequency (%)
0 83466
41.6%
2 1
 
< 0.1%
3 1
 
< 0.1%
20 1
 
< 0.1%
46 1
 
< 0.1%
669 1
 
< 0.1%
759 1
 
< 0.1%
5000 1
 
< 0.1%
7790 1
 
< 0.1%
29634 1
 
< 0.1%
ValueCountFrequency (%)
1.790682932 × 10121
< 0.1%
6.819932803 × 10101
< 0.1%
3.98360464 × 10101
< 0.1%
3.777576256 × 10101
< 0.1%
3.6525104 × 10101
< 0.1%
3.236956363 × 10101
< 0.1%
3.050190129 × 10101
< 0.1%
2.751613912 × 10101
< 0.1%
2.7 × 10101
< 0.1%
2.641529826 × 10101
< 0.1%

Valor Pendiente de Pago
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct62651
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76775385
Minimum-1.398232 × 108
Maximum6.0072888 × 1011
Zeros73931
Zeros (%)36.8%
Negative342
Negative (%)0.2%
Memory size3.1 MiB
2023-07-18T19:14:14.756512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.398232 × 108
5-th percentile0
Q10
median4247383
Q318925015
95-th percentile1.172 × 108
Maximum6.0072888 × 1011
Range6.0086871 × 1011
Interquartile range (IQR)18925015

Descriptive statistics

Standard deviation1.8308169 × 109
Coefficient of variation (CV)23.846404
Kurtosis60431.785
Mean76775385
Median Absolute Deviation (MAD)4247383
Skewness206.50185
Sum1.5413349 × 1013
Variance3.3518905 × 1018
MonotonicityNot monotonic
2023-07-18T19:14:15.050540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73931
36.8%
12000000 762
 
0.4%
9000000 758
 
0.4%
6000000 752
 
0.4%
1 693
 
0.3%
18000000 597
 
0.3%
10000000 582
 
0.3%
3000000 574
 
0.3%
15000000 565
 
0.3%
4000000 486
 
0.2%
Other values (62641) 121059
60.3%
ValueCountFrequency (%)
-139823200 1
< 0.1%
-130438820 1
< 0.1%
-88271811 1
< 0.1%
-80483971 1
< 0.1%
-72765756 1
< 0.1%
-49673584 1
< 0.1%
-49599996 1
< 0.1%
-49524712 1
< 0.1%
-43750000 1
< 0.1%
-41284040 1
< 0.1%
ValueCountFrequency (%)
6.007288838 × 10111
< 0.1%
2.133450183 × 10111
< 0.1%
1.89810009 × 10111
< 0.1%
1.865070701 × 10111
< 0.1%
1.459392215 × 10111
< 0.1%
1.432822371 × 10111
< 0.1%
1.179277155 × 10111
< 0.1%
9.689301 × 10101
< 0.1%
9.518281839 × 10101
< 0.1%
9.468352547 × 10101
< 0.1%

Valor Pagado
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct52548
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40502412
Minimum0
Maximum1.653768 × 1012
Zeros94006
Zeros (%)46.8%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-07-18T19:14:15.313559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3200000
Q318290000
95-th percentile61168918
Maximum1.653768 × 1012
Range1.653768 × 1012
Interquartile range (IQR)18290000

Descriptive statistics

Standard deviation3.711574 × 109
Coefficient of variation (CV)91.638343
Kurtosis196317.12
Mean40502412
Median Absolute Deviation (MAD)3200000
Skewness440.67125
Sum8.1312238 × 1012
Variance1.3775781 × 1019
MonotonicityNot monotonic
2023-07-18T19:14:15.563571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 94006
46.8%
12000000 868
 
0.4%
6000000 803
 
0.4%
9000000 740
 
0.4%
15000000 711
 
0.4%
10000000 655
 
0.3%
18000000 623
 
0.3%
8000000 531
 
0.3%
21000000 492
 
0.2%
7500000 434
 
0.2%
Other values (52538) 100896
50.3%
ValueCountFrequency (%)
0 94006
46.8%
2 1
 
< 0.1%
3 1
 
< 0.1%
20 1
 
< 0.1%
46 1
 
< 0.1%
669 1
 
< 0.1%
759 1
 
< 0.1%
4034 1
 
< 0.1%
5000 1
 
< 0.1%
7790 1
 
< 0.1%
ValueCountFrequency (%)
1.653768028 × 10121
< 0.1%
5.818033414 × 10101
< 0.1%
3.98360464 × 10101
< 0.1%
3.6525104 × 10101
< 0.1%
3.255813844 × 10101
< 0.1%
2.751613912 × 10101
< 0.1%
2.7 × 10101
< 0.1%
2.605258884 × 10101
< 0.1%
2.583476979 × 10101
< 0.1%
2.523522504 × 10101
< 0.1%

Valor Pendiente de Ejecucion
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct62687
Distinct (%)31.3%
Missing362
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean71743284
Minimum-1.398232 × 108
Maximum2.1334502 × 1011
Zeros73496
Zeros (%)36.6%
Negative338
Negative (%)0.2%
Memory size3.1 MiB
2023-07-18T19:14:15.800241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.398232 × 108
5-th percentile0
Q10
median4299750
Q319000000
95-th percentile1.1757528 × 108
Maximum2.1334502 × 1011
Range2.1348484 × 1011
Interquartile range (IQR)19000000

Descriptive statistics

Standard deviation1.1168111 × 109
Coefficient of variation (CV)15.566768
Kurtosis14059.621
Mean71743284
Median Absolute Deviation (MAD)4299750
Skewness98.583765
Sum1.4377139 × 1013
Variance1.2472669 × 1018
MonotonicityNot monotonic
2023-07-18T19:14:16.018244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73496
36.6%
12000000 762
 
0.4%
9000000 758
 
0.4%
6000000 752
 
0.4%
1 694
 
0.3%
18000000 597
 
0.3%
10000000 584
 
0.3%
3000000 572
 
0.3%
15000000 566
 
0.3%
4000000 486
 
0.2%
Other values (62677) 121130
60.3%
ValueCountFrequency (%)
-139823200 1
< 0.1%
-130438820 1
< 0.1%
-88271811 1
< 0.1%
-80483971 1
< 0.1%
-72765756 1
< 0.1%
-49673584 1
< 0.1%
-49599996 1
< 0.1%
-49524712 1
< 0.1%
-43750000 1
< 0.1%
-41284040 1
< 0.1%
ValueCountFrequency (%)
2.133450183 × 10111
< 0.1%
1.865070701 × 10111
< 0.1%
1.432822371 × 10111
< 0.1%
1.179277155 × 10111
< 0.1%
9.689301 × 10101
< 0.1%
9.518281839 × 10101
< 0.1%
9.468352547 × 10101
< 0.1%
8.487290598 × 10101
< 0.1%
8.022376125 × 10101
< 0.1%
7.127073569 × 10101
< 0.1%

Saldo CDP
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct70969
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3536735 × 109
Minimum0
Maximum4.3012743 × 1013
Zeros10804
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-07-18T19:14:16.253237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111213750
median30000000
Q32.2630977 × 108
95-th percentile4.8087477 × 109
Maximum4.3012743 × 1013
Range4.3012743 × 1013
Interquartile range (IQR)2.1509602 × 108

Descriptive statistics

Standard deviation2.4131277 × 1011
Coefficient of variation (CV)71.95476
Kurtosis30184.975
Mean3.3536735 × 109
Median Absolute Deviation (MAD)24949000
Skewness170.40369
Sum6.7328014 × 1014
Variance5.8231855 × 1022
MonotonicityNot monotonic
2023-07-18T19:14:16.498564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10804
 
5.4%
12000000 1259
 
0.6%
15000000 1235
 
0.6%
10000000 1100
 
0.5%
6000000 994
 
0.5%
9000000 963
 
0.5%
20000000 939
 
0.5%
18000000 872
 
0.4%
21000000 808
 
0.4%
24000000 768
 
0.4%
Other values (70959) 181017
90.2%
ValueCountFrequency (%)
0 10804
5.4%
1 3
 
< 0.1%
9 1
 
< 0.1%
17 1
 
< 0.1%
32 1
 
< 0.1%
328 1
 
< 0.1%
342 1
 
< 0.1%
876 1
 
< 0.1%
2600 1
 
< 0.1%
42250 1
 
< 0.1%
ValueCountFrequency (%)
4.301274292 × 10131
 
< 0.1%
4.301225174 × 10131
 
< 0.1%
4.30116355 × 10131
 
< 0.1%
4.301160446 × 10133
< 0.1%
6.750000001 × 10124
< 0.1%
6.1232 × 10121
 
< 0.1%
6.11985 × 10121
 
< 0.1%
6.11645 × 10121
 
< 0.1%
6.11325 × 10122
< 0.1%
6.10665 × 10121
 
< 0.1%

EsPostConflicto
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
No
199676 
Si
 
1018
ND
 
65

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters401518
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 199676
99.5%
Si 1018
 
0.5%
ND 65
 
< 0.1%

Length

2023-07-18T19:14:16.718647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-18T19:14:16.933665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 199676
99.5%
si 1018
 
0.5%
nd 65
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 199741
49.7%
o 199676
49.7%
S 1018
 
0.3%
i 1018
 
0.3%
D 65
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 200824
50.0%
Lowercase Letter 200694
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 199741
99.5%
S 1018
 
0.5%
D 65
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
o 199676
99.5%
i 1018
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 401518
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 199741
49.7%
o 199676
49.7%
S 1018
 
0.3%
i 1018
 
0.3%
D 65
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 401518
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 199741
49.7%
o 199676
49.7%
S 1018
 
0.3%
i 1018
 
0.3%
D 65
 
< 0.1%

Destino Gasto
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Inversión
130404 
Funcionamiento
69505 
No Definido
 
850

Length

Max length14
Median length9
Mean length10.739524
Min length9

Characters and Unicode

Total characters2156056
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInversión
2nd rowInversión
3rd rowFuncionamiento
4th rowInversión
5th rowFuncionamiento

Common Values

ValueCountFrequency (%)
Inversión 130404
65.0%
Funcionamiento 69505
34.6%
No Definido 850
 
0.4%

Length

2023-07-18T19:14:17.187829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-18T19:14:17.465403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
inversión 130404
64.7%
funcionamiento 69505
34.5%
no 850
 
0.4%
definido 850
 
0.4%

Most occurring characters

ValueCountFrequency (%)
n 470173
21.8%
i 271114
12.6%
e 200759
9.3%
o 140710
 
6.5%
I 130404
 
6.0%
v 130404
 
6.0%
r 130404
 
6.0%
s 130404
 
6.0%
ó 130404
 
6.0%
a 69505
 
3.2%
Other values (10) 351775
16.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1953597
90.6%
Uppercase Letter 201609
 
9.4%
Space Separator 850
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 470173
24.1%
i 271114
13.9%
e 200759
10.3%
o 140710
 
7.2%
v 130404
 
6.7%
r 130404
 
6.7%
s 130404
 
6.7%
ó 130404
 
6.7%
a 69505
 
3.6%
t 69505
 
3.6%
Other values (5) 210215
10.8%
Uppercase Letter
ValueCountFrequency (%)
I 130404
64.7%
F 69505
34.5%
N 850
 
0.4%
D 850
 
0.4%
Space Separator
ValueCountFrequency (%)
850
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2155206
> 99.9%
Common 850
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 470173
21.8%
i 271114
12.6%
e 200759
9.3%
o 140710
 
6.5%
I 130404
 
6.1%
v 130404
 
6.1%
r 130404
 
6.1%
s 130404
 
6.1%
ó 130404
 
6.1%
a 69505
 
3.2%
Other values (9) 350925
16.3%
Common
ValueCountFrequency (%)
850
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2025652
94.0%
None 130404
 
6.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 470173
23.2%
i 271114
13.4%
e 200759
9.9%
o 140710
 
6.9%
I 130404
 
6.4%
v 130404
 
6.4%
r 130404
 
6.4%
s 130404
 
6.4%
a 69505
 
3.4%
t 69505
 
3.4%
Other values (9) 282270
13.9%
None
ValueCountFrequency (%)
ó 130404
100.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Distribuido
168100 
Recursos Propios
32659 

Length

Max length16
Median length11
Mean length11.813388
Min length11

Characters and Unicode

Total characters2371644
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDistribuido
2nd rowDistribuido
3rd rowDistribuido
4th rowDistribuido
5th rowDistribuido

Common Values

ValueCountFrequency (%)
Distribuido 168100
83.7%
Recursos Propios 32659
 
16.3%

Length

2023-07-18T19:14:17.718978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-18T19:14:17.991158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
distribuido 168100
72.0%
recursos 32659
 
14.0%
propios 32659
 
14.0%

Most occurring characters

ValueCountFrequency (%)
i 536959
22.6%
s 266077
11.2%
o 266077
11.2%
r 233418
9.8%
u 200759
 
8.5%
D 168100
 
7.1%
t 168100
 
7.1%
b 168100
 
7.1%
d 168100
 
7.1%
R 32659
 
1.4%
Other values (5) 163295
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2105567
88.8%
Uppercase Letter 233418
 
9.8%
Space Separator 32659
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 536959
25.5%
s 266077
12.6%
o 266077
12.6%
r 233418
11.1%
u 200759
 
9.5%
t 168100
 
8.0%
b 168100
 
8.0%
d 168100
 
8.0%
e 32659
 
1.6%
c 32659
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
D 168100
72.0%
R 32659
 
14.0%
P 32659
 
14.0%
Space Separator
ValueCountFrequency (%)
32659
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2338985
98.6%
Common 32659
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 536959
23.0%
s 266077
11.4%
o 266077
11.4%
r 233418
10.0%
u 200759
 
8.6%
D 168100
 
7.2%
t 168100
 
7.2%
b 168100
 
7.2%
d 168100
 
7.2%
R 32659
 
1.4%
Other values (4) 130636
 
5.6%
Common
ValueCountFrequency (%)
32659
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2371644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 536959
22.6%
s 266077
11.2%
o 266077
11.2%
r 233418
9.8%
u 200759
 
8.5%
D 168100
 
7.1%
t 168100
 
7.1%
b 168100
 
7.1%
d 168100
 
7.1%
R 32659
 
1.4%
Other values (5) 163295
 
6.9%

Dias Adicionados
Real number (ℝ)

SKEWED  ZEROS 

Distinct152
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42787621
Minimum0
Maximum1160
Zeros199682
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-07-18T19:14:18.240204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1160
Range1160
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.7873292
Coefficient of variation (CV)20.537083
Kurtosis2199.6875
Mean0.42787621
Median Absolute Deviation (MAD)0
Skewness34.722273
Sum85900
Variance77.217154
MonotonicityNot monotonic
2023-07-18T19:14:18.498309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 199682
99.5%
30 111
 
0.1%
31 79
 
< 0.1%
214 58
 
< 0.1%
1 43
 
< 0.1%
4 38
 
< 0.1%
244 33
 
< 0.1%
92 31
 
< 0.1%
61 30
 
< 0.1%
91 25
 
< 0.1%
Other values (142) 629
 
0.3%
ValueCountFrequency (%)
0 199682
99.5%
1 43
 
< 0.1%
2 18
 
< 0.1%
3 10
 
< 0.1%
4 38
 
< 0.1%
5 14
 
< 0.1%
6 14
 
< 0.1%
7 21
 
< 0.1%
8 11
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
1160 1
 
< 0.1%
496 1
 
< 0.1%
436 1
 
< 0.1%
412 1
 
< 0.1%
379 1
 
< 0.1%
369 1
 
< 0.1%
366 1
 
< 0.1%
365 1
 
< 0.1%
337 3
< 0.1%
335 1
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
No Definido
126739 
Mujer
38638 
Hombre
35188 
Otro
 
194

Length

Max length11
Median length11
Mean length8.9621038
Min length4

Characters and Unicode

Total characters1799223
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Definido
2nd rowNo Definido
3rd rowNo Definido
4th rowNo Definido
5th rowHombre

Common Values

ValueCountFrequency (%)
No Definido 126739
63.1%
Mujer 38638
 
19.2%
Hombre 35188
 
17.5%
Otro 194
 
0.1%

Length

2023-07-18T19:14:18.738557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-18T19:14:19.370114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 126739
38.7%
definido 126739
38.7%
mujer 38638
 
11.8%
hombre 35188
 
10.7%
otro 194
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 288860
16.1%
i 253478
14.1%
e 200565
11.1%
N 126739
7.0%
126739
7.0%
D 126739
7.0%
f 126739
7.0%
n 126739
7.0%
d 126739
7.0%
r 74020
 
4.1%
Other values (8) 221866
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1344986
74.8%
Uppercase Letter 327498
 
18.2%
Space Separator 126739
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 288860
21.5%
i 253478
18.8%
e 200565
14.9%
f 126739
9.4%
n 126739
9.4%
d 126739
9.4%
r 74020
 
5.5%
j 38638
 
2.9%
u 38638
 
2.9%
m 35188
 
2.6%
Other values (2) 35382
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
N 126739
38.7%
D 126739
38.7%
M 38638
 
11.8%
H 35188
 
10.7%
O 194
 
0.1%
Space Separator
ValueCountFrequency (%)
126739
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1672484
93.0%
Common 126739
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 288860
17.3%
i 253478
15.2%
e 200565
12.0%
N 126739
7.6%
D 126739
7.6%
f 126739
7.6%
n 126739
7.6%
d 126739
7.6%
r 74020
 
4.4%
j 38638
 
2.3%
Other values (7) 183228
11.0%
Common
ValueCountFrequency (%)
126739
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1799223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 288860
16.1%
i 253478
14.1%
e 200565
11.1%
N 126739
7.0%
126739
7.0%
D 126739
7.0%
f 126739
7.0%
n 126739
7.0%
d 126739
7.0%
r 74020
 
4.1%
Other values (8) 221866
12.3%

Presupuesto General de la Nacion – PGN
Real number (ℝ)

SKEWED  ZEROS 

Distinct25930
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42256030
Minimum0
Maximum1.7997072 × 1012
Zeros162435
Zeros (%)80.9%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-07-18T19:14:19.631293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile51342468
Maximum1.7997072 × 1012
Range1.7997072 × 1012
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.0671607 × 109
Coefficient of variation (CV)96.250422
Kurtosis190980.04
Mean42256030
Median Absolute Deviation (MAD)0
Skewness432.14748
Sum8.4832783 × 1012
Variance1.6541796 × 1019
MonotonicityNot monotonic
2023-07-18T19:14:19.931425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 162435
80.9%
30000000 95
 
< 0.1%
20000000 90
 
< 0.1%
15000000 90
 
< 0.1%
41973750 89
 
< 0.1%
10000000 86
 
< 0.1%
41210000 81
 
< 0.1%
40000000 80
 
< 0.1%
12000000 79
 
< 0.1%
27500000 78
 
< 0.1%
Other values (25920) 37556
 
18.7%
ValueCountFrequency (%)
0 162435
80.9%
3670 1
 
< 0.1%
7790 1
 
< 0.1%
34605 1
 
< 0.1%
59800 1
 
< 0.1%
67400 1
 
< 0.1%
68100 1
 
< 0.1%
79900 1
 
< 0.1%
85000 1
 
< 0.1%
99000 1
 
< 0.1%
ValueCountFrequency (%)
1.79970725 × 10121
< 0.1%
1.865070701 × 10111
< 0.1%
7.127073569 × 10101
< 0.1%
6.912008998 × 10101
< 0.1%
5.579352 × 10101
< 0.1%
4.50088007 × 10101
< 0.1%
4.20078 × 10101
< 0.1%
3.6525104 × 10101
< 0.1%
3.509137069 × 10101
< 0.1%
3.3404 × 10101
< 0.1%
Distinct27150
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22669220
Minimum0
Maximum1.8 × 1011
Zeros110781
Zeros (%)55.2%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-07-18T19:14:20.220425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312180874
95-th percentile41910000
Maximum1.8 × 1011
Range1.8 × 1011
Interquartile range (IQR)12180874

Descriptive statistics

Standard deviation5.8576632 × 108
Coefficient of variation (CV)25.839721
Kurtosis56461.525
Mean22669220
Median Absolute Deviation (MAD)0
Skewness212.2501
Sum4.55105 × 1012
Variance3.4312218 × 1017
MonotonicityNot monotonic
2023-07-18T19:14:20.455283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 110781
55.2%
12000000 1178
 
0.6%
9000000 1099
 
0.5%
6000000 1051
 
0.5%
15000000 878
 
0.4%
18000000 872
 
0.4%
10000000 802
 
0.4%
21000000 772
 
0.4%
8000000 641
 
0.3%
3000000 554
 
0.3%
Other values (27140) 82131
40.9%
ValueCountFrequency (%)
0 110781
55.2%
1 1
 
< 0.1%
9149 1
 
< 0.1%
13685 1
 
< 0.1%
18674 1
 
< 0.1%
39401 2
 
< 0.1%
46470 1
 
< 0.1%
48787 1
 
< 0.1%
51492 2
 
< 0.1%
73696 1
 
< 0.1%
ValueCountFrequency (%)
1.8 × 10111
< 0.1%
1.179277155 × 10111
< 0.1%
9.699 × 10101
< 0.1%
3.20584563 × 10101
< 0.1%
2.6268 × 10101
< 0.1%
2.426897467 × 10101
< 0.1%
2.211735194 × 10101
< 0.1%
2.053847255 × 10101
< 0.1%
2.049898819 × 10101
< 0.1%
2 × 10101
< 0.1%

Recursos Propios
Real number (ℝ)

SKEWED  ZEROS 

Distinct18605
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12829422
Minimum0
Maximum9.4683525 × 1010
Zeros166430
Zeros (%)82.9%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-07-18T19:14:20.742361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile32255254
Maximum9.4683525 × 1010
Range9.4683525 × 1010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.6386927 × 108
Coefficient of variation (CV)28.362093
Kurtosis32856.094
Mean12829422
Median Absolute Deviation (MAD)0
Skewness156.50931
Sum2.575622 × 1012
Variance1.3240085 × 1017
MonotonicityNot monotonic
2023-07-18T19:14:21.034391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 166430
82.9%
12000000 253
 
0.1%
6000000 247
 
0.1%
15000000 241
 
0.1%
10000000 231
 
0.1%
9000000 196
 
0.1%
24000000 178
 
0.1%
20000000 169
 
0.1%
18000000 162
 
0.1%
7500000 157
 
0.1%
Other values (18595) 32495
 
16.2%
ValueCountFrequency (%)
0 166430
82.9%
1 1
 
< 0.1%
14567 1
 
< 0.1%
61832 1
 
< 0.1%
71444 1
 
< 0.1%
102266 1
 
< 0.1%
133333 1
 
< 0.1%
141200 1
 
< 0.1%
142800 1
 
< 0.1%
150000 2
 
< 0.1%
ValueCountFrequency (%)
9.468352547 × 10101
< 0.1%
6.9486 × 10101
< 0.1%
4.929783084 × 10101
< 0.1%
4.0900925 × 10101
< 0.1%
3.37060732 × 10101
< 0.1%
3.040726901 × 10101
< 0.1%
2.268010974 × 10101
< 0.1%
2.08848622 × 10101
< 0.1%
1.85726061 × 10101
< 0.1%
1.8 × 10101
< 0.1%

Interactions

2023-07-18T19:13:58.690077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:33.954382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:36.588445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:39.420177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:42.048242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:44.846534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:47.460257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:50.341225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:53.005413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:55.802508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:58.959719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:34.284426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:36.904503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:39.689272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:42.350869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:45.154895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:47.771374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:50.647784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:53.293455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:56.100564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:59.223345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:34.561594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:37.247024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:39.933376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:42.702479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:45.435555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:48.065516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:50.898789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:53.577551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:56.360463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:59.456346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:34.837595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:37.540431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:40.182322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:43.023540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:45.664020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:48.299190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:51.139248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:53.829069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:56.626497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:59.692358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:35.112271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:37.850481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:40.471324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:43.302370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:45.893446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:48.552378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:51.384202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:54.107242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:56.928674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:59.891345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:35.360224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:38.169151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:40.777446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:43.540175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:46.123528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:48.793443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:51.608255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:54.365240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:57.222409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:14:00.129341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:35.609285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:38.431150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:41.038445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:43.783676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:46.344669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:49.015151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:51.851615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:54.616298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:57.532215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:14:00.377442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:35.836622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:38.686160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:41.301517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:44.024274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:46.607752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:49.256379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:52.099560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:55.024441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:57.838831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:14:00.654421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:36.082255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:38.951179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:41.555246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:44.294585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:46.893754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:49.797446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:52.389074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:55.304451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:58.159829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:14:00.933503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:36.338386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:39.207177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:41.825257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:44.593350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:47.204427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:50.092475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:52.736297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:55.566507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T19:13:58.431357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-18T19:14:21.351395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Valor del ContratoValor FacturadoValor Pendiente de PagoValor PagadoValor Pendiente de EjecucionSaldo CDPDias AdicionadosPresupuesto General de la Nacion – PGNRecursos Propios (Alcaldías, Gobernaciones y Resguardos Indígenas)Recursos PropiosDepartamentoOrdenSectorRamaEntidad CentralizadaEstado ContratoTipo de ContratoModalidad de ContratacionCondiciones de EntregaEs GrupoEs PymeHabilita Pago AdelantadoLiquidaciónObligación AmbientalEsPostConflictoDestino GastoOrigen de los RecursosGénero Representante Legal
Valor del Contrato1.0000.3310.3810.2820.3820.5340.0080.244-0.0450.0470.0000.0000.0000.0010.0000.0000.0000.0150.0000.0270.0000.0000.0050.0000.0000.0020.0000.000
Valor Facturado0.3311.000-0.5800.918-0.5830.174-0.0220.186-0.044-0.0380.0000.0000.0000.0040.0000.0000.0000.0180.0000.0130.0000.0000.0000.0000.0000.0000.0000.002
Valor Pendiente de Pago0.381-0.5801.000-0.6831.0000.2080.0210.0080.0140.0810.0000.0020.0000.0000.0060.0000.0310.0120.0000.0210.0000.0000.0120.0000.0000.0000.0000.000
Valor Pagado0.2820.918-0.6831.000-0.6860.149-0.0190.175-0.058-0.0400.0000.0000.0000.0040.0000.0000.0000.0180.0000.0130.0000.0000.0000.0000.0000.0000.0000.002
Valor Pendiente de Ejecucion0.382-0.5831.000-0.6861.0000.2090.0210.0090.0140.0810.0000.0080.0080.0000.0100.0150.0330.0310.0000.0540.0000.0000.0220.0000.0000.0000.0000.000
Saldo CDP0.5340.1740.2080.1490.2091.0000.0430.244-0.094-0.0150.0070.0040.0250.0000.0060.0040.0000.0150.0040.0000.0010.0000.0030.0000.0000.0080.0030.007
Dias Adicionados0.008-0.0220.021-0.0190.0210.0431.000-0.029-0.0470.0050.0190.0140.0400.0110.0200.0230.0240.0400.0130.0100.0000.0000.0100.0030.0000.0290.0050.015
Presupuesto General de la Nacion – PGN0.2440.1860.0080.1750.0090.244-0.0291.000-0.404-0.1770.0000.0000.0000.0010.0000.0000.0000.0190.0000.0270.0000.0000.0070.0000.0000.0000.0000.000
Recursos Propios (Alcaldías, Gobernaciones y Resguardos Indígenas)-0.045-0.0440.014-0.0580.014-0.094-0.047-0.4041.000-0.3670.0000.0000.0000.0000.0040.0040.0060.0240.0000.0190.0050.0000.0070.0000.0000.0000.0000.000
Recursos Propios0.047-0.0380.081-0.0400.081-0.0150.005-0.177-0.3671.0000.0000.0190.0110.0050.0070.0070.0750.0190.0000.0270.0000.0000.0140.0000.0000.0000.0210.000
Departamento0.0000.0000.0000.0000.0000.0070.0190.0000.0000.0001.0000.2750.1950.1310.3250.2130.0550.0910.1310.0610.1060.0420.2510.2060.0600.1790.1740.166
Orden0.0000.0000.0020.0000.0080.0040.0140.0000.0000.0190.2751.0000.5690.2410.1220.0710.1720.1640.1560.0220.1420.0070.0990.2240.0530.0680.1990.159
Sector0.0000.0000.0000.0000.0080.0250.0400.0000.0000.0110.1950.5691.0000.3950.3770.1780.1180.2090.1510.0590.2220.0140.3020.4860.1940.3330.3890.166
Rama0.0010.0040.0000.0040.0000.0000.0110.0010.0000.0050.1310.2410.3951.0000.0420.0470.1520.1350.0880.0160.0800.0070.1010.0460.0440.1470.3130.047
Entidad Centralizada0.0000.0000.0060.0000.0100.0060.0200.0000.0040.0070.3250.1220.3770.0421.0000.0580.0860.1090.1480.0140.0360.0070.0340.1200.0400.0450.1380.057
Estado Contrato0.0000.0000.0000.0000.0150.0040.0230.0000.0040.0070.2130.0710.1780.0470.0581.0000.1700.2140.0490.1760.0820.0250.0790.0150.0290.0520.0420.033
Tipo de Contrato0.0000.0000.0310.0000.0330.0000.0240.0000.0060.0750.0550.1720.1180.1520.0860.1701.0000.5140.0510.3490.3990.2650.2420.0450.0120.1890.1560.114
Modalidad de Contratacion0.0150.0180.0120.0180.0310.0150.0400.0190.0240.0190.0910.1640.2090.1350.1090.2140.5141.0000.0570.4860.5170.1110.3230.0700.0560.2490.1890.130
Condiciones de Entrega0.0000.0000.0000.0000.0000.0040.0130.0000.0000.0000.1310.1560.1510.0880.1480.0490.0510.0571.0000.0150.0920.0000.1810.4300.0140.0960.0780.056
Es Grupo0.0270.0130.0210.0130.0540.0000.0100.0270.0190.0270.0610.0220.0590.0160.0140.1760.3490.4860.0151.0000.0100.0330.0850.0090.0130.0000.0070.053
Es Pyme0.0000.0000.0000.0000.0000.0010.0000.0000.0050.0000.1060.1420.2220.0800.0360.0820.3990.5170.0920.0101.0000.0110.1650.0420.0000.0800.0240.177
Habilita Pago Adelantado0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0070.0140.0070.0070.0250.2650.1110.0000.0330.0111.0000.0080.0000.0000.0030.0070.023
Liquidación0.0050.0000.0120.0000.0220.0030.0100.0070.0070.0140.2510.0990.3020.1010.0340.0790.2420.3230.1810.0850.1650.0081.0000.0370.0050.1040.0490.096
Obligación Ambiental0.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.2060.2240.4860.0460.1200.0150.0450.0700.4300.0090.0420.0000.0371.0000.0090.0870.0430.083
EsPostConflicto0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.0530.1940.0440.0400.0290.0120.0560.0140.0130.0000.0000.0050.0091.0000.1950.0310.009
Destino Gasto0.0020.0000.0000.0000.0000.0080.0290.0000.0000.0000.1790.0680.3330.1470.0450.0520.1890.2490.0960.0000.0800.0030.1040.0870.1951.0000.1210.044
Origen de los Recursos0.0000.0000.0000.0000.0000.0030.0050.0000.0000.0210.1740.1990.3890.3130.1380.0420.1560.1890.0780.0070.0240.0070.0490.0430.0310.1211.0000.071
Género Representante Legal0.0000.0020.0000.0020.0000.0070.0150.0000.0000.0000.1660.1590.1660.0470.0570.0330.1140.1300.0560.0530.1770.0230.0960.0830.0090.0440.0711.000

Missing values

2023-07-18T19:14:01.424349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-18T19:14:02.945134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-18T19:14:04.453412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DepartamentoOrdenSectorRamaEntidad CentralizadaEstado ContratoTipo de ContratoModalidad de ContratacionFecha de FirmaFecha de Inicio del ContratoFecha de Fin del ContratoFecha de Inicio de EjecucionFecha de Fin de EjecucionCondiciones de EntregaEs GrupoEs PymeHabilita Pago AdelantadoLiquidaciónObligación AmbientalValor del ContratoValor FacturadoValor Pendiente de PagoValor PagadoValor Pendiente de EjecucionSaldo CDPEsPostConflictoDestino GastoOrigen de los RecursosDias AdicionadosGénero Representante LegalPresupuesto General de la Nacion – PGNRecursos Propios (Alcaldías, Gobernaciones y Resguardos Indígenas)Recursos Propios
2403711Distrito Capital de BogotáTerritorialPlaneaciónEjecutivoCentralizadaterminadoPrestación de serviciosContratación directa2021-04-152021-04-192021-12-30NaTNaTNo DefinidoNoNoNoNoNo680000006800000079999667200004799996.076000000NoInversiónDistribuido0No Definido0680000000
2286389Valle del CaucaTerritorialdeportesEjecutivoDescentralizadaCerradoPrestación de serviciosContratación directa2021-11-242021-11-262021-12-27NaTNaTComo acordado previamenteNoNoNoNoNo36000000360000003600000.03600000NoInversiónDistribuido0No Definido036000000
1152985Distrito Capital de BogotáTerritorialSalud y Protección SocialEjecutivoDescentralizadaterminadoPrestación de serviciosContratación régimen especial2020-03-242020-02-012020-02-292020-02-012020-02-29A convenirNoSiNoNoNo23217000232170002321700.00NoFuncionamientoDistribuido0No Definido000
473693BoyacáTerritorialServicio PúblicoEjecutivoDescentralizadaterminadoPrestación de serviciosContratación directa2020-10-062020-10-062020-12-31NaTNaTA convenirNoNoNoNoNo10475839601047583960104758396.0104758396NoInversiónDistribuido0No Definido000
435778RisaraldaTerritorialServicio PúblicoEjecutivoDescentralizadaCerradoPrestación de serviciosContratación directa2022-08-162022-08-182022-12-28NaTNaTA convenirNoNoNoNoNo11711700117117000117117000.011711700NoFuncionamientoDistribuido0Hombre0117117000
1909582Valle del CaucaTerritorialServicio PúblicoEjecutivoCentralizadaterminadoPrestación de serviciosContratación directa2022-01-282022-02-142022-12-30NaTNaTA convenirNoSiNoNoNo37485000037485000037485000.037485000NoInversiónDistribuido0Mujer0374850000
927373Distrito Capital de BogotáTerritorialAmbiente y Desarrollo SostenibleCorporación AutónomaDescentralizadaterminadoPrestación de serviciosContratación directa2021-05-202021-05-252022-01-06NaTNaTA convenirNoNoNoNoNo130240001302400013024000013024000.013024000NoInversiónDistribuido0No Definido0130240000
1414269Distrito Capital de BogotáNacionalEducación NacionalEjecutivoDescentralizadaterminadoPrestación de serviciosContratación régimen especial2020-06-192020-06-232020-12-31NaTNaTNo DefinidoNoNoNoNoNo397344082876014339734408039734408.039734408NoFuncionamientoRecursos Propios0No Definido0039734408
297285Valle del CaucaTerritorialdeportesEjecutivoCentralizadaterminadoPrestación de serviciosContratación directa2019-02-212019-02-252019-04-25NaTNaTNo DefinidoNoNoNoNoNo31840000318400003184000.06368000NoInversiónDistribuido0No Definido000
2439291BolívarTerritorialServicio PúblicoEjecutivoDescentralizadaterminadoPrestación de serviciosContratación directa2022-09-292022-09-292022-12-28NaTNaTA convenirNoNoNoNoNo96000009600000960000009600000.0639061200NoInversiónDistribuido0Mujer096000000
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